Thursday, 24 November 2022

Data scientist job roles to more than double in five years! Are you ready with the right skills?

 Digitisation has been at the forefront since the 2020 pandemic and organizations, globally, have invested in emerging technologies to augment their operations and efficiency. As this journey of digital evolution continues, data science and analytics has taken a centre stage.

Increasingly, companies are realising the importance of data to optimize their business operations. Data enables business leaders and decision-makers to take informed decisions and ensure prosperity and growth.

In fact, data science has emerged as one of the fastest-growing business segments, having witnessed over 650% growth since 2012, and expected to grow to 230.80 billion dollars by 2026.

This has significantly increased employment opportunities in the space and the demand for skilled resources. Data science-related job roles are one of the most in-demand tech jobs in the world right now and estimated to be the third-highest paying.

 Starting salaries for data science-focused job roles have also witnessed incredible growth with the average salary starting from $210,000 per annum and can go up to limitless amounts due to the potential entrepreneurship opportunity.

As more employers will continue to hire for this domain in 2023 as well, let’s take a deeper look at the top skill sets that data science candidates must have:


With numerous data sources and applications available, Data Scientists must know how to read and extract usable information and insights from raw data. This means they must know what the best application to use, when to use it and how.

They must be able to convert the raw data into a suitable format or structure for easy querying and analysis.


Data analytics as a job profile has witnessed 7x growth in the last decade. An industry agnostic profile, the demand for candidates are expected to have in-depth knowledge about deconstructing and interpreting data. Post the initial phase of sorting and processing the data, analysis process is exploratory data analysis (EDA) to figure and make sense of the data, and to modify the resources to get desired answers to problems.

This process is done by observing patterns, trends, outliers, and unexpected outcomes, among others. Data Wrangling, on the other hand, is a lengthy and time-consuming process however it will help in making better data-driven judgments.


Python and R Programming are the most common coding language required in Data Science roles for organizing unstructured data sets and generating necessary outcomes that are desired by companies, irrespective of their domain.

To manipulate the data and apply sets of algorithms as and when required, Data scientists should possess expert knowledge of these languages.

The demand for this skill has been high across industries like healthcare, finance, government, energy, hospitality and logistics. In the next five years, the demand for data scientist with knowledge of Python is expected to go above 10 million.

 Data scientist, data science, data scientist skills, data science jobs


The emerging technologies to look out for in the next few years, data science professionals adept at or building ML and AI technologies stand out and are treated as royalty in the tech world.

With a clear understanding of machine learning and AI concepts, an individual can work on different algorithms and data-driven models, and can simultaneously handle large data sets such as cleaning data by removing redundancies.

This allows significant optimisation and brings in critical efficiency needed by companies to reduce costs and ensure profitability.


To perform tasks and execute to get the desired output, data scientists are expected to have a strong command of numbers, statistics and probability.

Before creating high-quality models, it is required to understand these concepts without which making senses of reams of data would be impossible.

As the demand for data scientists continues to increase exponentially, it is critical for the industry to have access to skilled talent. Aspiring candidates need to focus on acquiring the required skill sets and continually upskill themselves.

In-demand roles that require specialization include data engineer, AI engineer, and business analyst – salaries for these roles average around Rs 45 lakhs and are climbing fast.

 Source: India Today

Sunday, 20 November 2022

Probability Distributions in Data Science

What Is Probability?

Probability denotes the possibility of something happening. It is a mathematical concept that predicts how likely events are to occur. The probability values are expressed between 0 and 1. The definition of probability is the degree to which something is likely to occur. This fundamental theory of probability is also applied to probability distributions.

What Are Probability Distributions?

A probability distribution is a statistical function that describes all the possible values and probabilities for a random variable within a given range. This range will be bound by the minimum and maximum possible values, but where the possible value would be plotted on the probability distribution will be determined by a number of factors. The mean (average), standard deviation, skewness, and kurtosis of the distribution are among these factors.

Types of Probability Distribution

The probability distribution is divided into two parts:

  1. Discrete Probability Distributions
  2. Continuous Probability Distributions

Discrete Probability Distribution

A discrete distribution describes the probability of occurrence of each value of a discrete random variable. The number of spoiled apples out of 6 in your refrigerator can be an example of a discrete probability distribution.

Each possible value of the discrete random variable can be associated with a non-zero probability in a discrete probability distribution.

Let's discuss some significant probability distribution functions.

1. Binomial Distribution

The binomial distribution is a discrete distribution with a finite number of possibilities. When observing a series of what are known as Bernoulli trials, the binomial distribution emerges. A Bernoulli trial is a scientific experiment with only two outcomes: success or failure.

Consider a random experiment in which you toss a biased coin six times with a 0.4 chance of getting head. If 'getting a head' is considered a ‘success’, the binomial distribution will show the probability of r successes for each value of r.

The binomial random variable represents the number of successes (r) in n consecutive independent Bernoulli trials.

2. Bernoulli's Distribution

The Bernoulli distribution is a variant of the Binomial distribution in which only one experiment is conducted, resulting in a single observation. As a result, the Bernoulli distribution describes events that have exactly two outcomes.

Here’s a Python Code to show Bernoulli distribution:


The Bernoulli random variable's expected value is p, which is also known as the Bernoulli distribution's parameter.

The experiment's outcome can be a value of 0 or 1. Bernoulli random variables can have values of 0 or 1.

3. Poisson Distribution

A Poisson distribution is a probability distribution used in statistics to show how many times an event is likely to happen over a given period of time. To put it another way, it's a count distribution. Poisson distributions are frequently used to comprehend independent events at a constant rate over a given time interval. Siméon Denis Poisson, a French mathematician, was the inspiration for the name.

The Python code below shows a simple example of Poisson distribution. 

It has two parameters:

  1. Lam: Known number of occurrences
  2. Size: The shape of the returned array

The below-given Python code generates the 1x100 distribution for occurrence 5.


Continuous Probability Distributions

A continuous distribution describes the probabilities of a continuous random variable's possible values. A continuous random variable has an infinite and uncountable set of possible values (known as the range). The mapping of time can be considered as an example of the continuous probability distribution. It can be from 1 second to 1 billion seconds, and so on.

The area under the curve of a continuous random variable's PDF is used to calculate its probability. As a result, only value ranges can have a non-zero probability. A continuous random variable's probability of equaling some value is always zero.

Now, look at some varieties of the continuous probability distribution.

4. Normal Distribution

Normal Distribution is one of the most basic continuous distribution types. Gaussian distribution is another name for it. Around its mean value, this probability distribution is symmetrical. It also demonstrates that data close to the mean occurs more frequently than data far from it. Here, the mean is 0, and the variance is a finite value.

In the example, you generated 100 random variables ranging from 1 to 50. After that, you created a function to define the normal distribution formula to calculate the probability density function. Then, you have plotted the data points and probability density function against X-axis and Y-axis, respectively.



Continuous Uniform Distribution

In continuous uniform distribution, all outcomes are equally possible. Each variable has the same chance of being hit as a result. Random variables are spaced evenly in this symmetric probabilistic distribution, with a 1/ (b-a) probability.

The below Python code is a simple example of continuous distribution taking 1000 samples of random variables.




Data Visualization-I in PYTHON

Making plots and static or interactive visualizations is one of the most important tasks in data analysis. It may be a part of the exploratory process; for example, helping identify outliers, needed data transformations, or coming up with ideas for models.

matplotlib.pyplot is a plotting library used for 2D graphics in python programming language. It can be used in python scripts, shell, web application servers and other graphical user interface toolkits.

Matploitlib is a Python Library used for plotting, this python library provides and objected-oriented APIs for integrating plots into applications.

Before start plotting let us understand some basics

  1.  With Pyplot, you can use the xlabel() and ylabel() functions to set a label for the x- and y-axis.
  2.  With Pyplot, you can use the grid() function to add grid lines to the plot.
  3.  You can use the keyword argument linestyle, or shorter ls, to change the style of the plotted line:
  4.  The plot() function is used to draw points (markers) in a diagram. By default, the plot() function draws a line from point to point.
  5.  You can use the keyword argument marker to emphasize each point with a specified marker: 

 Importing matplotlib :

from matplotlib import pyplot as plt
import matplotlib.pyplot as plt 

Basic plots in Matplotlib :

Matplotlib comes with a wide variety of plots. Plots helps to understand trends, patterns, and to make correlations. They’re typically instruments for reasoning about quantitative information. Some of the sample plots are covered here.

 a) Line Chart 

 Line charts are used to represent the relation between two data X and Y on a different axis

# importing the required libraries
import matplotlib.pyplot as plt
import numpy as np

# define data values
x = np.array([1, 2, 3, 4]) # X-axis points
y = x*2 # Y-axis points

plt.plot(x, y) # Plot the chart # display

The following is the output

b) Bar Chart

  1. A bar plot or bar chart is a graph that represents the category of data with rectangular bars with lengths and heights that is proportional to the values which they represent.
  2. The bar plots can be plotted horizontally or vertically.
  3. A bar chart describes the comparisons between the discrete categories. One of the axis of the plot represents the specific categories being compared, while the other axis represents the measured values corresponding to those categories.

The following programs show the comparison between year and product

import matplotlib.pyplot as plt
# Creating data
year = ['2010', '2002', '2004', '2006', '2008']
production = [25, 15, 35, 30, 10]
# Plotting barchart, production)
# Saving the figure.

 The following is the output 

c) scatter plots

Scatter plots show many points plotted in the Cartesian plane. Each point represents the values of two variables. One variable is chosen in the horizontal axis and another in the vertical axis.

import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.rand(50, 4), columns=['a', 'b', 'c', 'd'])
df.plot.scatter(x='a', y='b')

The following is the output


d) Pie Chart

  1. A Pie Chart is a circular statistical plot that can display only one series of data.
  2. The area of the chart is the total percentage of the given data.
  3. The area of slices of the pie represents the percentage of the parts of the data.
  4. The slices of pie are called wedges. The area of the wedge is determined by the length of the arc of the wedge. The area of a wedge represents the relative percentage of that part with respect to whole data.
  5. Pie charts are commonly used in business presentations like sales, operations, survey results, resources, etc as they provide a quick summary. 

# Import libraries
from matplotlib import pyplot as plt
import numpy as np

# Creating dataset
cars = ['AUDI', 'BMW', 'FORD','TESLA', 'JAGUAR', 'MERCEDES']

data = [23, 17, 35, 29, 12, 41]

# Creating plot
fig = plt.figure(figsize =(10, 7))
plt.pie(data, labels = cars)

# show plot

The following is the output

e) Box Plot

  1. Box plots are a measure of how well distributed the data in a data set is.
  2. It divides the data set into three quartiles. This graph represents the minimum, maximum, median, first quartile and third quartile in the data set.
  3. It is also useful in comparing the distribution of data across data sets by drawing boxplots for each of them.
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.rand(10, 5), columns=['A', 'B', 'C', 'D', 'E'])'True')

The following is the output




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